Agile methodologies work well with database developments only if great care is taken to do things right. It requires good judgement and leaves little room for error. Dev Nambi, in an extract from the book Tribal SQL, argues that Agile works for smart, curious, and experienced software engineers.

To effectively master Agile sprint deployments and to practice DevOps, one needs to be able to implement deployment automation. Otherwise deployments and releases will require manual steps and processes, which are not always accurately repeatable, prone to human errors, and cannot be handled with high frequency. Dealing with database deployments is tricky; unlike other software components and code or compiled code, a database is not a collection of files. It is not something you can just copy from your development to testing and to production because the

Creating an effective data migration testing strategy is critical to reducing risk and delivering a successful migration. This article offers thoughts and recommendations on how to create a more consistent data migration testing methodology using either a black box or a white box approach.

Often, an existing database application must evolve rapidly by incremental steps. Alex describes a tried and tested system to provide an automated approach to deploying both new and existing database systems, whilst dealing with common security and configuration issues.

Big Data is a big topic in software development today. When it comes to practice, software testers may not yet fully understand what is exactly Big Data. A tester knows that you need a plan for testing it. Since most Big Data lacks a traditional structure, how does Big Data quality look like? And what the are most appropriate software testing tools? This article tries to answer these questions.

Although many of the important tasks a DBA has to perform should be done 'by hand', keying in commands or using SSMS, the canny DBA with a heavy workload will always have an eye to automating routine tasks wherever possible, or using a tool. Although the likely candidates for automation are often obvious, it is not always so. Time can often be saved in surprising ways.

Source control will allow you to maintain branches in the development of your database, but the subsequent merge isn't pain-free. How, from the practical perspective, can the database developer support the rapid development and delivery of features in an application? Versioning, branching and merging is part of the solution, but what about the rest of the solution?

Continuous Delivery is fairly generally understood to be an effective way of tackling the problems of software delivery and deployment by making build, integration and delivery into a routine. The way that databases fit into the Continuous Delivery story has been less-well defined. Phil Factor explains why he's an enthusiast for databases being full participants, and suggests practical ways of doing so.

The various ways in which SOA design principles can be synergized with Big Data are explored. Complex event processing, Apache Hadoop metadata management, scalable Infrastructure-as-a-Service (IaaS), and front-end analytics are among the methods that can render Big Data-as-a-Service (BDaaS).